1. Journal

Journal ofPerformanceManagement

The Financial Crisis: Was It Just About Credit Or Was There Another Underlying Issue That We Could Have Seen Coming?

- RICH WEISSMAN -

Organizational Specificities That Affect The Use Of CorporatePerformance Measurements Process In The Banking Sector.

- GUMMA FAKHRI / KARIM MENACERE / ROGER PEGUM -

Strategic Positioning And Capacity Utilization:Factors In Planning For Profitable Growth In Banking

- ANJAN ROY -

Volume 23, Number 3

The Journal of Performance Management seeks articles from management informationprofessionals on subjects related to management information in the financial servides industry.

Manuscripts should be typed with double spacing and generous margins. Please contact AMIfs for complete Manuscript Guidelines prior to submitting your article.

Submit manuscripts to:AMIfs14247 Saffron CircleCarmel, IN 46032

(317) 815-5857 FAX: (317) 815-5877Email: [email protected]: www.amifs.org

All articles in the Journal reflect the views of the authors and should not be construedas the opinions of the Association for Management Information in Financial Services.Contributing authors are required to sign a copyright agreement.

AMIfs Research CommitteeJeff Nathasingh, BBVA Compass, ChairGreg Fitzgerald, AmTrustWilliam Di Filippo, Frost BankChris Rebant, Huntington

The Research Committee can be contacted by email at [email protected]

Copyright ©2011 by the Association for Management Information in Financial Services. All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopying, recording, or otherwise, without the prior written permission of the publisher.

For a complete list of previous Journal issues, refer to theAMIfs web site at www.amifs.org. Orders for previous issues

may be placed directly on the website under the Education page.

TABLE OF CONTENTS 1

Table of Contents

Prologue ......................................................................................................... 2

The Financial Crisis: Was It Just About Credit Or Was There AnotherUnderlying Issue That We Could Have Seen Coming? ............................. 3- RICH WEISSMAN -

Organizational Specificities That Affect The Use Of CorporatePerformance Measurements Process In The Banking Sector. ................... 5- GUMMA FAKHRI / KARIM MENACERE / ROGER PEGUM -

Strategic Positioning And Capacity Utilization:Factors In Planning For Profitable Growth In Banking ................................ 23- ANJAN ROY -

ASSOCIATION FORMANAGEMENT INFORMATION

IN FINANCIAL SERVICES

THE ORGANIZATION

The Association for Management Information in Financial Services (AMIfs) is the preeminent organi-zation for management information professionals in the financial services industry. Founded in 1980 (known then as NABCA), AMIfs has become the premier organization of its type, and counts among its members individuals who set the policies and advance the concepts of management information at major financial institutions worldwide.

ASSOCIATION MISSION

AMIfs is a not-for-profit professional association dedicated to developing and advancing the profession of management information for the financial services industry. Its goals are:

n Leadership: Develop opportunities for members to advance the profession by participating in the Association.

n Research: Identify and coordinate research activities that support the goals of the organization and advance the profession.

n Education & Training: Provide professional development opportunities for industry practitioners.

n Networking: Provide opportunities for members to interact and share experience, knowledge, and insights.

n Other Member Services: Provide related services that add value to membership.

n Infrastructure: Establish and maintain an organizational structure designed to accomplish the Association’s mission through ongoing involvement of industry professionals.

JOURNAL OF PERFORMANCE MANAGEMENT 2

Prologue

The Financial Crisis: Was It Just About Credit Or Was There AnotherUnderlying Issue That We Could Have Seen Coming?

The Financial Crisis: Was It Just About Credit Or Was There Another Underlying Issue That We Could Have Seen Coming?

Rich Weissman, DMAwww.DMAcorporation.com

3

1

How did the meltdown happen? Was it simply poor credit risk management, or was there another fundamental bubble waiting to burst? At DMA, we’ve conducted lot of research to understand meltdowns. For many years, we’ve looked at historical data, and conducted modeling and analysis. We’ve found that the banking and credit union industry had created an unsustainable bubble independent of the credit crisis, and the meltdown could have been predicted. And, this understanding has been translated in measurement systems that can help banks and credit unions develop new risk strategies to stay clear of future meltdowns. The objective is to learn from the past, and focus on those activities that can minimize a repeat. Let’s share our insights. We divide the last 80 years into three distinct cultural phases. Phase 1 began after the Great Depression (“Pre-Deregulation Phase”), where the industry operated under strict regulations. Institutions didn’t compete with each other on products, price, or the ability to sell more. The government determined what could be offered, and institutions differentiated themselves through branch networks and providing quality service to customers/members. This phase ended with an act of Congress in the 1980’s, and the industry quickly adopted the concept of marketing/selling as fundamental (“Sales Culture Phase” – Phase 2). Everyone got on the bandwagon to aggressively market/sell products, compete on price, and focus on three key measures for assessing success: balance sheet growth, sales volumes, cross-sell ratio. Unknowingly, banks and credit unions were creating a bubble in their income statements. Volume-driven cultures create environments in which most products sold, most customers/members served, and most marketing/sales activities are unprofitable. Each year, as banks and credit unions sold more, they created a dependency on a smaller group of products and customers/members for positive earnings. This group got smaller and smaller each year as the banks and credit unions sold more unprofitable products to unprofitable customers/members, creating a bubble waiting to burst. Why? Because it wasn’t a sustainable way of earning profitability. And it burst with the credit crisis. Could this have been seen? Sure! We call it “Profit Risk”, a term we developed and coined some time ago that did indeed predict the meltdowns. How? It’s all in understanding the increasing concentration of the income statement in fewer and fewer products and customers/members as sales volumes increase over time, ultimately leading to unhealthy concentration levels that can act as predictors of meltdowns.


2

The problem is that although institutions track the bottom line, very few know how they got there, and most do not understand how to manage the income statement from a “Profit Risk” perspective. Sure they manage credit risk and asset-liability risk, but they are not managing the income statement for its concentrations in its product lines, customers/members, markets, branches, or sales officers. This lack of “Profit Risk” management forms the foundation for the meltdown of the income statement. What should banks and credit unions do? First, they need to understand that just selling more only creates greater concentrations, which lead to meltdowns. Second, they need to enter the next phase (“Profitability Phase”). Phase 3 is about understanding the income statement in the most micro analytic way, and correlating its components with future earnings growth/loss, and then managing to maximize future income growth and minimize future income losses. An institution can watch its earnings grow, but if those earnings are heavily concentrated, then earnings can quickly head south before the institution sees it coming. A “Profit Risk” system and analytics show that there are tipping points at which earnings will become volatile and decline over time as concentrations increase, even in a rising earnings environment. Quantifying and measuring these points, and developing specific strategies and tactics to minimize “Profit Risk” and ensure long-term sustainability of the income statement, is what banks and credit unions need to do. What should you do differently? Get started and learn about “Profit Risk” and how to measure and manage it, and have a new crystal ball. Phase 2 thinking has to go. You need to understand your income statement in altogether new ways, and you need to adopt a Phase 3 culture as a fundamental culture, based on assessing concentrations and developing strategies to minimize the concentrations so that the income statement is sustainable. That’s a good starting point.

1

How did the meltdown happen? Was it simply poor credit risk management, or was there another fundamental bubble waiting to burst? At DMA, we’ve conducted lot of research to understand meltdowns. For many years, we’ve looked at historical data, and conducted modeling and analysis. We’ve found that the banking and credit union industry had created an unsustainable bubble independent of the credit crisis, and the meltdown could have been predicted. And, this understanding has been translated in measurement systems that can help banks and credit unions develop new risk strategies to stay clear of future meltdowns. The objective is to learn from the past, and focus on those activities that can minimize a repeat. Let’s share our insights. We divide the last 80 years into three distinct cultural phases. Phase 1 began after the Great Depression (“Pre-Deregulation Phase”), where the industry operated under strict regulations. Institutions didn’t compete with each other on products, price, or the ability to sell more. The government determined what could be offered, and institutions differentiated themselves through branch networks and providing quality service to customers/members. This phase ended with an act of Congress in the 1980’s, and the industry quickly adopted the concept of marketing/selling as fundamental (“Sales Culture Phase” – Phase 2). Everyone got on the bandwagon to aggressively market/sell products, compete on price, and focus on three key measures for assessing success: balance sheet growth, sales volumes, cross-sell ratio. Unknowingly, banks and credit unions were creating a bubble in their income statements. Volume-driven cultures create environments in which most products sold, most customers/members served, and most marketing/sales activities are unprofitable. Each year, as banks and credit unions sold more, they created a dependency on a smaller group of products and customers/members for positive earnings. This group got smaller and smaller each year as the banks and credit unions sold more unprofitable products to unprofitable customers/members, creating a bubble waiting to burst. Why? Because it wasn’t a sustainable way of earning profitability. And it burst with the credit crisis. Could this have been seen? Sure! We call it “Profit Risk”, a term we developed and coined some time ago that did indeed predict the meltdowns. How? It’s all in understanding the increasing concentration of the income statement in fewer and fewer products and customers/members as sales volumes increase over time, ultimately leading to unhealthy concentration levels that can act as predictors of meltdowns.

Organizational Specificities That Affect The Use Of Corporate Performance Measurements Process In The Banking Sector. 5

Organizational Specificities That Affect The Use Of CorporatePerformance Measurements Process In The Banking Sector.

Gumma Fakhri1

Karim Menacere2

Roger Pegum3

1

Organizational Specificities that affect the Use of Corporate Performance Measurements Process in the Banking Sector.

Gumma Fakhri1

Karim Menacere 2 Roger Pegum3

Abstract

The paper tries to explore the use of multiple-perspectives of performance measures in the context of performance evaluation within the banking sector. It will also examine the impact of some organizational specificity of the use performance measures. Hence, this paper identifies the reality of measures perspective taken from the performance measurement literature, and investigates the impact of five chosen organizational individuality (e.g. the nature of bank services, the customers’ demands, size of bank, and the listing on stock market) on the use of performance measures. Based on a scale survey in a sample of 55 respondents from sampled banks, the study develops hypotheses concerning the paper objectives, and uses descriptive and inferential statistical analysis in order to determine and assess the underlying impact of using multiple performance measures. The findings of this study have revealed that most of the respondents sample put their emphasis greatly on financial measures as a primary approach to evaluate performance, although several banks are adopting the non financial measures, and they tend to implement customer related measures and learning and employee growth measures more frequently. The study has also discovered that there are significant differences in the use of financial and non financial measures according to banks' characteristics (nature and particularities) of banks, which lead to have varied perspectives in the use of performance measures in the banking sector. Keywords: Performance Measurements, Banking sector, Management Accounting.

1.1 Introduction

Drury, (2004) suggests that the management accounting literature underlines the

importance of performance measurement process and how performance measurement

systems play an important role in the financial success of the organisation, and as a source

which provides appropriate information about internal activities. Therefore, firms focus on

the use of performance measures to allow managers to make basic decisions in order to

achieve organisational objectives. In this regard, Anthony and Govindarajan (2001) 1 Faculty of Business and Law at Liverpool John Moores University +44(0)151 231 3858 E: [email protected] 2 Faculty of Business and Law at Liverpool John Moores University +44 (0) 151 231 3593 E: [email protected] 3 Faculty of Business and Law at Liverpool John Moores University +44 (0) 151 231 3849 E: [email protected]


2

stresses that performance measures are important for managers to track and to measure

performance for their subunits, as well as for employees at lower levels to understand the

financial impact of their operating decisions. In addition, the importance of use

contemporary performance measures like quality service, customer satisfaction, comes

from the highly competitive financial industry, particularly the banking sector, as well as

in other services and even in manufacturing organisations (Hussain, 2002). Consequently,

measuring the performance of financial and non-financial require special consideration in

this particular kind of service organisation. Although a lot has been written about the need

for accurate measurement of multi-dimensional performance measures, and there is plenty

of research concerning performance measurement, however comparatively very little is

known about performance measurement systems in services and the banking sector

especially in developing countries. Therefore, the main purpose of this paper is to examine

the impact of the characteristics of banks on the use of financial and non-financial with

particular reference to developing countries’ Banks.

1.2 A Brief Literature Review and Development of Hypothesis

1.2.1 Financial Measures:

Financial performance measures are used to provide financial information to the managers

and other users, also to evaluate efficiency and effectiveness. The more popular financial

measures used for example are: return on investment; return on assets; return on capital

employed; and earnings per share (Ittner and Larcker, 2003). Although the use of financial

performance measures is important in performance measurement, researchers seem to

suggest that there are limited in scope. For example, Ittner and Larcker, (1998); Neely,

(1999); Kaplan and Norton, (1996): (2001); and Banker et al, (2000) conclude that there is

agreement about the limitations of financial measures such as, they are too financially

oriented, internal looking, historical and focusing on inputs not outputs, and are short term

oriented. These limitations indicate that financial measures should be expanded to

incorporate the valuation of the company's intangible and intellectual assets such as; high

quality products, motivated and skilled employees, responsive and predictable processes,

and satisfied and loyal customers in order to reflect the assets and capabilities that are

critical for success in today's competitive environment (Kaplan and Atkinson, 1998).

These types of measures can be categorized as non-financial performance measures.


3

Furthermore, Kaplan and Norton (1996) argue that measurement using only financial

measures can damage an organisation’s capacities and they recommend that a combination

of financial and non-financial measures are better suited for evaluating performance.

1.2.2 Non -Financial Measures:

Several recent studies have provided empirical evidence on the positive impact of non-

financial performance measures on the organisations’ financial performance in the long-

term (Anderson and Lanen, 1999; and Banker et al, 2000). Non-financial performance

measures provide managers with timely information concentrated on the causes and

drivers of success and can be used to design integrated evaluation systems (Fitzgerald et

al, 1991; Kaplan and Norton, 1996; Banker et al, 2000). Fisher (1995) states that there are

three main reasons for the emergence of non-financial performance measures: the

limitations of traditional financial performance measures, competitive pressures, and the

growth of other initiatives. In addition, Neely (1999) points out several reasons for this

performance measures revolution such as increasing competition, changing organisational

roles, changing external demands and the power of information technology. This in turn

has led to the recognition that financial performance measures do not present a clear

picture of organisational performance (Bourne and Neely, 2002). Most studies of non-

financial performance measures are related to manufacturing with very few studies

including services firms (Kald and Nilsson, 2000). Several studies (Fitzgerald et al, 1991;

Kaplan and Norton, 2001; Hussain, et al 2002; Lorenzo, 2008) have emphasised the need

to use multidimensional performance measures in the service sector such as the banking

sector. Berry et al (1993) studied performance evaluation in UK bank lending decisions,

they argue that although manufacturing companies tend to emphasise the importance of

non-financial performance measures, bankers are concerned with more financial

performance measures. Ostinelli and Toscano (1994) have assessed the use of non

financial measures namely customer satisfaction and improvement in quality management

as an operational tool of control in three Italian banks. They found that the management

control system was able to integrate both financial and non-financial measures to evaluate

performance. Hussain et al (2002) carried out research on the role of management

accounting practices in non-financial performance measures in financial institutions

(including banks) in three countries Finland, Sweden and Japan. Their study found that


4

contextual factors such as economic, normative, coercive factors have affected the role

and the use of non-financial performance measures in the financial sector in three different

countries. Al-Enizi et al (2006) examined the use of non-financial performance measures

in the Gulf Cooperation Council Countries in four service companies (one of them was a

bank). They suggested that non-financial performance measures have a positive impact on

long-term profitability. Hussain and Hoque (2002) assessed the role of management

accounting in non-financial performance among Japanese financial institutions-banks.

They reported that management accounting has played a key role in measuring

performance in different banks in Japan, but its role in non-financial performance

measures has been less significant than its role in financial performance measures. The

findings concluded that non financial performance measures are needed and the contextual

factors affected the use of non-financial performance measurement in the sample studied.

The above discussion suggests that there are relatively few empirical studies which

directly examine the use of financial and non financial measures for performance

measurement purposes in the banking industry in developing countries. In addition, the

conclusions from related previous studies provide two main arguments regarding the use

of financial and non financial measures. The first argument points out that the use of

financial measures is more common and standardized than non financial measures across

the organization's sub-units as financial outcomes are the primary performance objectives.

The second argument concludes that non financial measures have greater use beside

financial measures in performance measurement systems, because non financial measures

are better measures to driving future financial performance, and they reflect the value of

long term aspects. Over the last decade, the balanced use of financial and non financial

measures for performance measurement have been strongly recommended by scholars and

professionals (e.g. Kaplan and Norton 1996). It could therefore be argued that if financial

measures are still fundamental for performance measurement in the banking sector

context, this paper sets the following hypothesis:

H.1 The banking sector tends to use financial measures rather than nonfinancial

measures more frequently.


5

1.3 Banking Characteristics Influencing Performance Measures

Since the 1980, there have been many studies that focus on different aspects of

management accounting practices especially in performance measurement such as the

relationship with contextual factors (e.g. organistaional size, customers’ demand, nature of

organization, and the joining to stock market) (see for instance Hussain and Hoque, 2002).

The different aspects of the literature will now be investagiated in more detail.

1.3.1 The Impact of Size of Organisation

In response to such economic pressures, management accounting practices become

adaptive to their environment with various degrees of responsiveness, but the

characteristics of the company (e.g. size and type) are a key determinant to the degree of

possible change and adaptation to the economic pressures (Granlund and Lukka, 1998;

and Hussain and Gunasekaran, 2002). As far as the impact of size of organisation of

performance measurement systems, several previous studies (Chenhall, 2003; Ezzamel,

1990) suggest that top management in large firms will implement a multiplicity of

performance measures relative to small firms to motivate managers of different

responsibility centers .For example, Chenhall (2003) indicates that size indeed affects the

design of performance measurement systems: larger organizations use more sophisticated

performance evaluation systems and tend to introduce non-financial measures. In

addition, organisational size might influence the shape of control systems used which

tends to be more sophisticated within bigger firms than smaller ones (Libby and

Waterhouse, 1996; and Speckbacher et al, 2003). In considering the impact of size of bank

on the use of financial and non financial in the banking sector, this study argues that the

size of service of a bank might impact accordingly on the use of financial and non

financial measures in the banking sector. Previous research has indicated that size indeed

affects the design of performance measurement systems: larger organizations use more

sophisticated performance evaluation systems (Chenhall 2003) and tend to introduce non-

financial measures (Hoque and James 2000). This leads to the following hypothesis:

H.2 Size of bank is positively associated with the use of financial and non financial

performance measures.


6

1.3.2 The Impact of Customers’ Demands

The change in customers’ attitudes and behaviour is one of the most important issues

mentioned by the literature as a motive for using non financial measures, for example,

Vaivio, (1999) indicates that customers’ demands could be a basic theme to any

organisation particularly in the service industries, devoting attention to customers leads to

the introduction of some non-financial measures (customer satisfaction measures) which

reflects the customer-organisation relationship. Moreover, there is an increase in the

number of organisations using customer satisfaction measures, due to managers’ belief

that these measurements affect financial performance outcome (Ittner and Larcker, 2003).

However, Anderson and Lanen, (1999) found positive associations between customer

satisfaction and return on investment in Swedish manufacturing organisations, but weaker

or negative connections in service organisations. Given the importance of customer needs,

performance measurement systems should track the change of customer performance

regularly.

H.3 Customers’ demands tend to affect the use of financial/non financial measures

more frequently.

1.3.3 Nature of Banking Industry

It could be argued that the nature of the banking industry is service oriented and depends

on human resources and this nature forced banks management to be very aware about

achieving a high level of quality, on-time delivery, customer satisfaction and loyalty and

employee satisfaction and loyalty in change of business environment. Hussain and

Gunasekaran, (2002) stress that the nature of the banking industry is considered to be one

of the motives for using range of performance measures as mentioned by their study’s

conclusion.

Cobb et al (1995) conclude that banking the activities, like bad loans in multinational

banks, has an impact on practice of non financial performance measures. In addition, Mills

and Morris (1986) argue that customers of services organizations is essential to the

production activities, so, some non financial measures (like quality) could be determined

simply in manufacturing organisations, however in service organizations (like a bank), it

is difficult to assess the quality of its services because of their intangibility and transitory

nature. In this case Smith’s (1998) points out that measurement of the quality of service


7

organisations is notoriously difficult. Seemingly the nature of services affects

performance measurement systems. In considering the impact of the nature of financial

industry impact on non financial performance measures is investigated. This substantive

hypothesis arose from the above discussion and there was no contradictory evidence.

Therefore, the fifth formal hypothesis is:

H.4 “The nature of the banking sector as a service oriented industry is one of

the major motives that affect the use of financial/non financial measures more

frequently.

1.3.4 The Impact of the Stock Market.

Larson and Kenny, (1995) argue that the development of accounting information systems

are fundamental for the improvement of the stock market, because users of accounting

information (e.g. investors) require reliable and fitting information. In addition, Adhikari

and Tondkar, (1992) indicates that there is a significant relationship between the

development of the stock market and accounting information systems particularly in

developing countries. The growing number of listed companies on the stock market

creates a need for new information and services such as specific information such these

about performance information. Furthermore, Doupnik and Salter (1995) suggest that, as

the level of activities increase in the stock market, users of information (i.e. investors and

managers) want more financial and non-financial information about the companies’

activities to assist in making decisions. Relating to the above, this study expects that the

development or establishment of stock market may increase the need for using financial

and non financial measures for performance measurement purposes. Therefore, the

rationale for joining the stock market is that banks aim to improve the adoption and the

uses of non financial performance measures more than non other banks that have not

jointed yet. A null hypothesis statement is used as the basis for this rationale:

H.5 There is no difference between Listed and Unlisted banks in stock market

regarding the use of financial and non financial measures more frequently.

1.4 Research method and survey instrument

The research has been conducted through a questionnaire preceded by an introductory

letter clarifying the purposes and objectives of the entire project. The sample consists of


8

ninety five managers from different types in the banking sector. Among this group of

banks, the researcher contacted managers (include chief executive officer, senior and

branch manager) directly in order to select a list of banks prepared to cooperate with the

research. The survey was carried out by sending a questionnaire during the second half of

2009. After three follow ups by phone calls made to non-respondents to increase survey

response rate, 83 questionnaires (55 usable) were sent back. The final response rate is

about 57% represents an acceptable target when the questionnaire involves top and middle

management levels. The questionnaire was developed and refined as follows: nearly all

items in the performance measures and characteristics factors were adapted from

previously published works. A preliminary draft of the questionnaire was discussed with

my supervision team and some research students at LJMU to assess the content validity

prior to pilot testing; and a pilot test was conducted with a group of five branches, whose

inputs were used to improve the clarity, comprehensiveness and relevance of the survey

instrument.

The questionnaire was structured in two parts. In the first part banks were asked to

indicate on a five point Likert scale – from 1 (not at all important/used), through 3

(moderately), up to5 (extensively) – the extent to which they used a set of performance

measures coming from academic/practitioner management accounting literature (Kaplan

and Norton 1996, 2000; Gosselin 2005). The second part listed some characteristics

related to banks such as the nature of banking services, and size of banks.

o Sample Features

Data was analysed using the SPSS package v15.0. The reliability of the questionnaire was

also verified. Internal consistency was established using Cronbach’s Alpha it was equal

(.815). The first empirical evidence of the survey is shown displayed through the use of

descriptive statistics. Table (1) gives an account about some general information date of

establishing, type of business, ownership, the total of assets, and the state of banks in the

stock market. Table (2) describes the distribution of respondents by the evaluation of the

importance to success of banks and the extent of current used of performance measures. In

addition, adjusting these measures as the size of bank and listing on stock market.


9

1.5 Findings of hypotheses testing

1.5.1 The use of financial and non financial measures

To test H1, the financial and non financial performance measures are ranked according to

the mean of the extent to which respondents from sampled banks are ranking them as

important to success of long term and are using them in aforementioned practices. Table

(1) accounts for the overall diverse measurements, the columns highlight this indicator

which calculates by average standardised rating of importance and using for each category

(financial and non financial measures). This indicator shows that if the level of overall

diverse measurements is up to 3 that means banks use diverse sets of performance

measures at a high level, however if the rate is less than that it means it is not a high level

of use for diverse sets of performance measures. From the table, it could be noted clearly

that the sampled banks are still relying on financial performance measures.

The highest rate of overall diverse measurement colum relate to financial measures which

ranked by mean (3.530) and other the non financial measures are ranked less than the level

of absenteeism (ranked +3). Therefore H1 is confirmed.

1.5.2 The impact of banking Individualities on performance measures

A factor analysis is undertaken in order to classify the measures into categories and to find

out the underlying themes among the 8 items. Principal Component Analysis (table 5)

reveals two interpretable factors with Eigen values greater than 1 that account for 64% of

the variance. The two factors are labelled as follow: customers’ demand (4 items); nature

of banking services (4 items);

To test the remaining four hypotheses a bivariate correlation is undertaken between the

four factors, two of them coming from the Principal Component Analysis (PCA)

(customers’ demand, and the nature of banking services), while the other two factors the

joining of stock market and size of organisation come from general information that

categorize the respondents to listed and unlisted banks and small and large banks

according to the total of assets.

Table (6) shows all the results of this analysis. Kendall’s tau (t) association coefficients

help to determine whether there are some associations among the four factors. These

estimates are accompanied by p-values from statistical significance tests. The size of bank


10

is positively correlated with non financial performance measures while it is so with

financial performance measures but it is not significant. Thus, H.2 (Size is positively

associated with the use of financial and non financial performance measures) is accepted.

With regard to the nature of banking services, it is positively correlated with quality,

financial, employee, and customer measures respectively, but less correlated with

community measures. These results sustain the idea that the nature of banking services

tend to use more non financial measures. Hence H.3 is confirmed.

The customers’ demands are positively correlated with financial and non financial

performance measures (even if these values are not statistically significant). So H.4 (The

customers’ demands are tend to use financial and non financial measures for performance

measurement) is not confirmed.

Banks that are listed in stock market are positively correlated with all performance

measures while unlisted banks are negatively correlated with non financial performance

measures (even if this value is statistically significant). Overall these results appear

coherent with the notion given that listed banks are positively correlated with the use of

non financial performance measures while, at the opposite end, unlisted banks are

negatively correlated. Therefore, H5 (The joining of stock market tends to affect the use of

financial/non financial measures more frequently) is confirmed.

1.6 The Findings and the Literature

1.6.1 The use of financial and non financial measure

In terms of the use of financial and non financial measures this paper provides evidence

that the banking sector is still reliant on financial measures with more attention for some

non financial measures. These results are consistent with findings for number of previous

studies (i.e. Mohamed and Hussain 2005; Ong and Teh 2009; Ismail 2007; Banker et al,

2004; Chen et al, 2006; Frigo and Krumwiede, 2000).

The possible reasons for above result are that many sampled banks still adopt conventional

accounting practices in addition to insufficiency of qualified accountants and dominance

of Central Bank as monitor. Regulations exclude sampled banks from requiring

permission to create new internal change.


11

1.6.2 The impact of bank size on the use of financial and non financial

measure

The size of bank as measured by total of assets has emerged as one of the motives

encouraging the use of financial and non financial measures. The results indicate that there

is a significant relationship between size of bank (total of assets) with the extent of using

financial and non financial performance measures. as this result has already been achieved

by some of previous study such as Verbeeten, 2004.

1.6.3 The impact of customers’ demands on the use of financial and

non financial measure

The results indicate that there i a weak relationship between customer’s demand and the

extent of using financial and non financial performance measures. This result is nearly

similar for Al-Enizi et al’s study in 2006.

1.6.4 The impact of nature of banking services on the use of financial

and non financial measure

The nature of the banking sector emerged as a purpose leading to the use of financial and

non financial measures. This finding is in agreement with previous findings or statements

in the literature (see, for example, Cobb et al, 1995 and Hussain and Gunasekaran, 2002).

1.6.5 The impact of joining the stock market on the use of financial

and non financial measure

The results confirm that there is relationship but not significant between joining the stock

market and the extent of using financial and non financial performance measures. The

study did not find support for this finding in the literature.

1.7 Limitations of the study

The aim of this paper is to contribute to a better understanding of what performance

measures are used by managers in the sampled banks. Specifically this paper upgrades the

existing theory, establishes relationships between contingencies factors and performance

measures with contingency theory and shows some results that it would be interesting to


12

develop further. However, this paper has some limitations. Finally, the sample comes from

the banking sector without considering other perspectives (e.g., manufacturing, another

services). In addition the sample was chosen from the banking sector in developing

country which is not be able to generalize the findings to other banking sector within

developed countries like the UK. Furthermore the paper does not consider how these

contingency relationships may impact on the organisational performance and what

combinations of performance measures can lead to improve financial results and

organizational behaviour with more regular use.

o Acknowledgement

Special thanks are owed to my two supervisors who assisted in this paper, Dr. Karim

Menacere and Dr Roger Pegum for their guidance, helpful and insightful comments. In

addition, I am also grateful for the financial support provided by Libyan Bureau Cultural

affair in London.

o References

- Adhikari, A. and Tondkar, R. H. (1992)"Environmental factors influencing accounting disclosure requirements of global stock exchanges." Journal of International Financial Management and Accounting Vol.4 (2).pp.75-105.

- Al-Arbah, S. A. (1985), “The Banking System in Socialist People’s Libyan Arab Jamahiriya”, The Institute of Banking Business and Management Publishing, Al-Dar Arabia for Cyclopaedia, Beirut.

- Al-Enizi, M. N., Innes, J., and Kouhy, R,. (2006) "Non-Financial performance measurement in the banking sector: four grounded theory case studies." International Journal of Accounting, Auditing and Performance Evaluation Vol.3 (3).pp 362-385.

- Anderson, S.W., and Lanen, W. (1999)"Economic transition, strategy and the evolution of management accounting practices: the case of India." Accounting, Organizations and Society Vol.24 (5-6).pp.379-412.

- Anthony, R. N., and Govindarajan, V., (2001) Management Control Systems. 10th ed. McGraw-Hill, New York.

- Banker, R D., Potter, G. and Srinivasan, D. (2000)"An empirical investigation of an incentive plan that includes non-financial performance measures." The Accounting Review Vol.75 (1):pp.65-92.

- Berry, R. H., Crum, R. E. and Waring, A. (1993) Corporate Performance Evaluation in Banking Lending Decisions CIMA, London.


13

- Bourne, M and Neely, A. (2002) "The success and failure of performance measurement initiatives: Perceptions of participating managers." International Journal of Operations & Production Management Vol.22 (11):pp. 1288-1310.

- Central Bank of Libya (2004), Annual Report No. 47th (2003), Musrath Al-Ezdihar Printing, Tripoli.

- Chen, H., Duh R, and Lin, J. C., (2006)"The determinants of implementation stages of balanced scorecard". Int. J. Management and Decision Making Vol.7 (4).pp.356-376.

- Chenhall, R, H. (2003)"Management control systems design within its organizational context: findings from contingency-based research and directions for the future." Accounting, Organizations and Society Vol.28:pp.127-168.

- Cobb, I. Helliar, C, and Innes, J. (1995)"Management accounting change in a bank” Management Accounting Research Vol.6:pp.155-175.

- Doupnik, T. S., And Salter, S. B. (1995)"External environment, culture, and accounting practice: A preliminary test of a general model of international accounting development." International journal of accounting Vol.17 (3):pp.33-52.

- Drury, C. (2004) Management and cost accounting. 6th ed. Thomson: London. - Ezzamel, L. (1990)"The impact of environmental uncertainty, managerial autonomy and

size on budget characteristics" Management Accounting Research Vol.1 (2):pp.181-197. - Farley, R. (1971), Planning for Development in Libya, the Exceptional Economy in the

Developing World, Praeger Publishers, New York. - Fisher, J. (1995)"Use of non-financial performance measures". In S.M. Young, Ed,

Readings in Management Accounting, Prentice Hall, Englewood Cliffs, NJ. Pp.329-335. - Fitzgerald, L., Johnston, R., Brignall, T.J., Silvestro, R. and Voss, C. (1991). Performance

measurement in service business. London: Charted Institute of Management Accountants (CIMA).

- Frigo, M and Krumwiede, K. (2000)"The balanced scorecard: a winning performance measurement system." Management Accounting (USA) Vol.81 (7):pp.50-54.

- Gosselin M., (2005) “An empirical study of performance measurement in manufacturing firms”, International Journal of Productivity and Performance Management, Vol. 54, No. 5/6, pp. 419-437.

- Granlund, M and Lukka, K. S. (1998)"It's a Small World of Management Accounting Practices. Authors." Journal of Management Accounting Research Vol.10:pp.153-179.

- Guenther, T. and Gruening, M. (2002)"Measuring performance: A cross-sectional empirical survey" In the 25th Annual Congress of the European Accounting Association Copenhagen Denmark.

- Higgins, B. (1959), Economic Development, W. W. Norton & Co., New York. - Hoque, Z and James, W. (2000)"Linking balanced scorecard measures to Size and Market

Factors: Impact on Organizational Performance" Journal of Management Accounting Research Vol.12: pp 1-17.


14

- Hussain, M. M., (2004)"Organisational strategic orientation and its impact on non financial performance measurement in the financial services industry". Management Research News Vol.27 (11/12).pp.115-133.

- Hussain, M. M., (2005)"Management accounting performance measurement systems in Swedish banks" European Business Review Vol.17, pp 566 – 589.

- Hussain, M and Gunasekaran, A. (2002)"An institutional perspective of non-financial management accounting measures: a review of the financial services industry." Managerial Auditing Journal Vol.17:pp.518-536.

Hussain, M., Islam, M, M., Gunasekaran, A., and Maskooki, K. (2002)"Accounting standards and practices of financial institutions in GCC countries". Managerial Auditing Journal Vol.17 (7):pp.350-362.

- Hussain, M and Hoque, Z. (2002)"Understanding non-financial performance measurement practices in Japanese banks: A new institutional sociology perspective". Accounting, Auditing & Accountability Journal Vol.15 (2).pp 162-183.

- Ismai T. H. (2007)"Performance evaluation measures in the private sector: Egyptian practices". Managerial Auditing Journal Vol.22 (5), pp.503-513.

- Ittner, C, D., and Larcker, D, F. (1998)"Innovations in Performance Measurement: Trends and Research Implications." Journal of Management Accounting Research Vol.10:pp.205-238.

- Ittner, C, D., and Larcker, D, F. (2003)"Coming up short on non financial performance measurement." Harvard Business Review Vol.81 (11):pp.88-95.

- Ittner, C, D., Larcker, D, F. and Taylor, R. (2003b)"Performance implications of strategic performance measurement in financial services firms" Accounting, Organizations and Society Vol.28 (7-8). pp 715-741.

- Kaplan, R and Atkinson, A. (1998) Advanced Management Accounting. 3rd ed. London: Prentice Hall International.

- Kaplan, R and Norton, D. (1992)"The balanced scorecard measures that drive performance." Harvard Business Review Vol.70 (1):pp.71-79.

- Kaplan, R and Norton, D (1996)"Using the balanced scorecard as a strategic management system." Harvard Business Review Vol.74 (1):pp.75-85.

- Kaplan, R and Norton, D. (2001)"Transforming the balanced scorecard from performance measurement to strategic management." Accounting Horizons Vol.15 (1):pp. 87-104.

- Larson, R, K. And Kenny, S. Y. (1995)"An Empirical analysis of international accounting standards, equity markets, and economic growth in developing countries." Journal of International Financial Management and Accounting Vol.6 (2):pp.130-157.

- Libby, T and Waterhouse, J. H. (1996)"Predicting change in management accounting systems." Journal of Management Accounting Research Vol.1:pp.137-150.


15

- Lorenzo, L. (2008)"Performance measures from a contingency perspective: field evidence." In 31st Annual Congress of the European Accounting Association. Rotterdam, April 23-25 European Accounting Association.

- Luxford, K. (2005), “Libya Beats a Path to Foreign Bankers”, Euromoney, (2005), Vol. 36, Issue 437.

- Masoud, A. M. and Al-Shrif, B. (2002), “The Libyan Commercial Banks – the Development and Activation”, Journal of Arab Banks Union, No. 259, pp. 81-88.

- Mills, P.K. and Morris, J.H. (1986), ``Clients as `partial' employees of service organisations: role of development in client participation'', Academy of Management Review, Vol.11, pp.726-35.

- Mohamed, E. K. A., and Hussain, M. M., (2005)"Management accounting and performance measurement practices in services sector in Oman". Int. J. Management and Decision Making, Vol.6 (2).pp.101-111.

- Neely, A. (1999)"The performance measurement revolution: why now and what next." international Journal of Operations & Production Management Vol.19 (2):pp.205-228.

- Ong, T. S and Teh, B. H., (2009)"the use of financial and non financial performance measures in the Malaysian manufacturing companies". The Icfai University Journal of Accounting Research Vol.VIII (1).pp.23-30.

- Ostinelli, C and Toscano, G. (1994)"Putting quality to work in banking through management accounting systems: three Italian banks’ " In the 17th Annual Congress of the European Accounting Association. Venice.

- Smith, M. (1998), ``Measuring organisational effectiveness'', Management Accounting, October,pp. 34-6.

- Speckbacher, G., Bischof, J. and Pfeiffer, T. (2003)"A descriptive analysis on the implementation of balanced scorecards in German-speaking countries." Management Accounting Research Vol.14 (4):pp.361-387.

- Vaivio, J. (1999)"Exploring a non-financial management accounting change." Management Accounting Research Vol.10 (4):pp.409-437.

- Vandewalle, D. (1998), Libya since Independence: Oil and State-Building, Cornell University Press.

- Verbeeten, F. (2004)"performance measurement and long-term managerial contract: empirical investigation", The 27th Annual Congress of the European Accounting Association (Prague- Czech Republic).


16

Appendices:

Table (1) Description of banks covered by the classification of survey Classification Of banks by year of establishing business typology:

Number (%) Before 1980

42 (76.5) Between 1991-2006

8 (23.5)

Classification of banks by ownership typology :

Number (%) State-owned (public bank)***

28 (51.5) Private

PAPP* 14 (25.0)

PSE** 12 (23.5)

Classification of banks by total of assets typology: Number (%) Less than 100

9 (16.5) Above 100 46 (83.5)

Classification of banks by Listing on Libyan Stock Market: Number (%) Listed Unlisted

23(41.2) 18(39.8) State-owned public bank*** = (the state owns more than 50% of their shares), PAPP*= private after process of privatisation (before that they were public), PSE**= private since establishing

Table (2) extent of use of performance measurements

Table (3) The customers’ demands

Mean Std. D

Comparisons of survey results by Typologies:

Listing on SM Size Ls Unls S L

Financial 3.706 0.774 3.600 3.814 3.686 3.765

Customer 2.985 0.837 2.857 3.116 3.020 2.882

Employee 2.324 0.742 2.257 2.388 2.412 2.059

Quality 3.206 0.907 3.171 3.234 3.196 3.235

Community 2.250 0.655 2.286 2.217 2.255 2.235 UNL un listed banks, L Listed banks, S small banks, L large banks, SM stock market

The level of importance Mean

Std. D


Listing on SM

Size

1/ 2 3 4/5 Ls Unls S L Customer demands in banking industry is a critical factor that affects the use of performance measures in different banks

70.6 26.5 2.9 2.118 0.763 1.657 2.608 2.314 1.529

The bank checks its customer satisfaction regularly, although it requests high cost to obtain.

76.4 23.5 0 1.897 0.756 1.371 2.454 2.098 1.294


Table (4) The nature of banking services

Table (5) Factor analysis Rotated Factor Matrix for 8 items

Components The nature of

banking services Customers’ demands

The banking industry is influenced by the changes in the level of progress in IT services. 0.5778 It is extremely difficult to predict another bank competitive move. 0,6464 It is intricate to predict and keep up with changes in the governmental (The Central Bank of Libya) regulations. 0,8329

Banking attributes and methods of service are constantly adapting to change and therefore unpredictable. 0,8651 Customer demands in banking industry is a critical factor that affects the use of performance measures in different banks 0,6326

The bank checks its customer satisfaction regularly, although it requests high cost to obtain. 0,7431

Bank’s management believe that customer attained is key factor that affects the use of performance measures. 0,5878

There is an increasing change in the customers’ demands and attitude regarding banking service. 0,5491

Eigenvalues 4.231 4.148 % of variance 10.07 9.875 Cumulative % 10.07 19.95

Table (6) Correlation Matrix (Tau (t) Kendall association measure

Size of bank

The nature of services

Customers’ demand

Listing on SM

Bank’s management believe that customer attained is key factor that affects the use of performance measures.

47.1 35.3 17.6 2.588 1.011 2.000 3.221 2.824 1.882

There is an increasing change in the customers’ demands and attitude regarding banking service.

1.5 51.5 47.1 3.603 0.756 3.657 3.550 3.706 3.294

Level of importance of

nature of banking services

Mean Std. D


Listing on SM

Size

1 2/3 4/5 Ls Unls S L The banking industry is influenced by the changes in the level of progress in IT services.

17.6 78 4.4 2.236 0.794 2.200 2.272 2.353 1.882

It is extremely difficult to predict another bank competitive move. 29.4 69.1 1.5 1.985 0.782 1.943 2.033 1.980 2.000

It is intricate to predict and keep up with changes in the governmental (The Central Bank of Libya) regulations.

47.1 53 0.0 1.588 0.604 1.457 1.721 1.667 1.353

Banking attributes and methods of service are constantly adapting to change and therefore unpredictable.

22.1 69.1 8.8 2.206 0.890 1.771 2.662 2.392 1.647


Financial 0.079 0.065 .334(**) 0.138 Customer .192(*) 0.122 .282(**) .358(**) Employee .432(**) 0.001 .330(**) .454(**)

Quality .578(**) 0.032 .370(**) .498(**) Community .407(**) 0.133 .210(*) .403(**)

** Correlation is significant at the 0, 01 level (two - tailed). * Correlation is significant at the 0, 05 level (two - tailed).

Strategic Positioning And Capacity Utilization: Factors In Planning For Profitable Growth In Banking 23

Strategic Positioning And Capacity Utilization:Factors In Planning For Profitable Growth In Banking

Anjan RoyAssistant Professor

National Institute of Bank ManagementMaharashtra, India

1

Strategic positioning and capacity utilization:

Factors in planning for profitable growth in banking

Anjan Roy Assistant Professor

National Institute of Bank Management Maharashtra, India

1.0 Introduction

Banks are motivated to grow and acquire large size and market share, often with the

expectation of gaining “too big to fail” advantage as well as higher profitability.

Higher profitability is expected to accrue from market power (Rhoades, 1982;

Gilbert, 1984) and/ or superior efficiency (Demsetz, 1973; Peltzman, 1977). While,

the market power hypothesis has been supported in studies of U.S. (Berger and

Hannan, 1989; Jeon and Miller, 2005; Tregenna, 2009) and European banking

(Molyneux and Thornton, 1992), the efficiency hypothesis has also been supported in

studies of US (Smirlock, 1985) and Portuguese banking (Mendes and Rebelo, 2003).

Maudos (1998) has reported both sources of profitability from their study of data in

Spanish and German banking.

Some other set of studies, interestingly, suggest that larger size may have

inverse impact upon profitability. Berger, Hanweck and Humphrey (1987) found that

bank costs reduce only slightly with size and very large banks often even face scale

inefficiencies. Schwartz (2007) reported that large banks do not have sustained

advantages in funding costs over small banks. Rapoport (2007) found smaller

community banks as having higher net interest margin than regional and large

diversified banks. Kosmidou, Tanna and Pasiouras (2005) found that size has

JOURNAL OF PERFORMANCE MANAGEMENT 24 2

negative impact over bank profits. Few others such as Goddard, Molyneux and

Wilson (2004) and Micco, Panizza and Yanez (2007) report only a weak relationship

between bank size and profitability. Some studies (Gallick, 1976; Hughes and Mester,

1993) have reported that the positive relationship between size and profitability exists

only over small to medium size groups.

While the size-profitability relationship remains inconclusive, the evidence of

negative impact of size on profitability arouses concern. Why must banks lose

profitability as they grow bigger? This study points towards certain factors behind the

size-profitability paradox in banking that have been left largely unaddressed. In

particular, it looks at the shortcomings in the planning practices that might prevent a

bank from achieving growth with profit. Planning function in banks may lack

strategic perspective of managing a multi-unit organization. In consequence,

therefore, they may fail to reap the benefits and efficiencies from diversification. By

making use of a well known growth-share matrix and an industrial cost-capacity

framework, the lack of planning view on strategic positioning and capacity

constraints is illustrated.

In section 2.0, studies discussing issues of planning in banks are reviewed to

construct the background to the problem of lack of profitable growth. In section 3.0

and 4.0, the frameworks for strategic positioning and capacity constraints

respectively, are discussed. In section 5.0, the industry and organizational context of

the bank is reported along with the findings from applying the frameworks to the

business and performance data of the bank. In section 6.0, the findings are put

together to reveal gaps in the planning process and the need for revised allocation of

Strategic Positioning And Capacity Utilization: Factors In Planning For Profitable Growth In Banking 25 3

growth target and/or resource budget. In concluding, it is suggested that banks

attending to both market positioning and capacity constraints during planning are

likely to be better at delivering profitable growth performance.

2.0 Planning for growth and profit in banking

Profitable growth of a bank can be constrained by external factors such as economic

environment, target market, industry structure, etc. as well as internal factors such as

branch network, technology and managerial capability for innovation and

differentiation, marketing, customer relationships, etc. While the external factors can

be beyond control of individual banks, bank management is responsible for astutely

positioning its business to achieve the right “fit” and foundation for performance.

Haslem (1968) has long back identified differences in management as one of

the key factor contributing to the difference of profitability between banks.

Subsequent studies such as by DeYoung (1994), Punt and Rooij (2001) have pointed

out to “X-efficiency” and management quality as a crucial factor explaining

profitability and financial performance of banks. Sarkis (1999) noticed that output

prices of banks tend to fall as they grow in size because their product mix evolves

from high margin geographically focused retail products towards diversified products

including those with marginally profitable activities. Pilloff and Rhoades (2002)

further reported that while bank concentration in local markets is significantly related

to profitability, though there is not enough evidence to support such relationship at

the bank level (Larreche, 1980 in Wind and Mahajan, 1891; Goddard et al., 2004).

These observations suggest that as banks expand into different markets and lines of


businesses to grow in size and complexity, planning issues on both cost as well as

revenue side become imminent. Banks need to cultivate their planning expertise

commensurate with the pace of growth (Hopkins and Hopkins, 1997). An important

aspect of planning in a multi-unit enterprise such as a bank is about allocative

efficiency. Allocative efficiency refers to achieving the right combination of inputs to

produce outputs, leading to the best possible utilization of market potential as well as

resource capacity. Several studies have reported the impact of allocative efficiency on

performance in banks to be non-trivial (DeYoung, 1994; Brissimis et al., 2010). Al

Shamsi et al. (2009) have pointed out that allocative inefficiency rather than technical

inefficiency has been the dominant source of inefficiency in UAE banking.

Literature on planning process inform that planning in banking has evolved

over time from performance budgeting to long range planning and strategic planning

(Wood, 1980; Austin, 1990; Bird, 1991) and impacted bank performance (Wood and

Laforge, 1979; Newkirk-Moore, 1995). However, the link between planning and

performance has been questioned (Gup and Whitehead, 1989; Whitehead and Gup,

1985). Prasad (1984) has pointed out that planning in banks is primarily based on

price information (i.e. cost of money) which is not enough to make decisions for

competitive operational advantage. Austin (1990) has suggested that bank planning

must enable market share penetration and, therefore, involve evaluation of market

potential through analysis of underlying financial and economic strengths. Planning

and target setting in banks have, however, been found to involve as many as twenty

indicators including several superfluous ones (Lovell and Pastor, 1997) and,

therefore, Rogers et al. (1999) have suggested that planning system design in banks


must address the required type and amount of information relevant to its strategy.

More recent literature on financial crisis and bank behavior, however, indicate that

banks are often prone to “herding” (Rotheli, 2001; Acharya and Yorulmazer, 2008),

such as in respect of credit (Uchida and Nakagawa, 2007), branching (Chang et at.,

1997) and pricing (Alhadeff, 1980) decisions, which may potentially undermine the

planning function.

Literature on strategic management of the firm has the positioning school and

the resource-based view as the two most acknowledged and practiced approaches to

achieve sustainable competitive advantage. While the former stresses the selection of

strategic positioning amidst competitive forces in the market (Porter, 1980), the latter

posits the role of the firm‟s resource capacity, in particular its managerial

competencies (Penrose, 1959; Barney, 1991), as the foundation for profitable growth.

Planning processes in banks may not address the strategic factors - market positioning

and capacity constraints - adequately thereby leading to growth at the expense of

profitability. The necessary requirements for growing profitably is that, the bank must

be positioned in markets where (a) growth potential exists, (b) it is competitively

positioned with respect to the peer banks and (c) it has the internal capacity to grow.

Strategic planning of growth with profitability involves assessment of these three

aspects, and appropriate allocation of targets and resources, for the business units of a

bank.

3.0 Strategic positioning


Banks operate in multiple markets that are usually defined in terms of product

categories (loans, deposits, payment, fund transfer, custody, etc), customer segments

(business, professionals, pensioners, salaried, etc), etc. Usually, however, banks are

organizationally structured into geographical units (country, region, district, city,

etc.), signifying the key markets where they are present. In order to have greater

stability and profit efficiency, banks must have competitive power in the markets they

operate (Ariss, 2010). Market power is indicated by the share of market, which has

been extensively studied for its effect upon profit performance. Market share stems

from the attractiveness of a bank in terms of the spread of branch network, match

between its product offerings and customer needs and distinctiveness of its service

differentiation. It is important for a bank to select and position itself in certain target

markets in order to define the space and potential for profitable growth. Positioning,

in strategy parlance, means making the choice of niche, or locating in the product

market domain (Mintzberg, 1987). The positioning school of strategy has typically

used analytical frameworks and matrices, such as Ansoff‟s matrix, Boston Consulting

Group‟s (BCG) matrix, etc. to create certain positions and performance categories.

For a multi-market business such as a bank, positioning is closely related to strategic

planning, which involves setting priority rules for allocating targets and resources to a

mix or portfolio of markets such that the potential and capacity is optimally utilized to

ensure net positive earnings along with growth.

Henderson (1968) formulated the BCG matrix relating profitability and

growth rate to provide a framework for evaluating decisions regarding investment and

growth for a portfolio of product businesses or market segments (Figure 1). This


planning matrix is based upon the principle of balancing cash flow requirements

amongst businesses keeping in view the life cycle stages of the business.

Accordingly, product sales growth rate of business units are plotted on the vertical

axis of the matrix with a horizontal line (usually representing 10% growth rate)

demarcating between high and low growth rates. The relative market share of product

business as against the nearest rival has been used as a proxy for profitability (Buzzel,

Gale and Sultan, 1975) and is plotted on the horizontal axis on a log scale where a

value of 1.0 indicates the demarcation between low or high market shares. In this way

the portfolio of growth markets has been classified into categories such as wildcat

(also called as problem child), star, cash cow and dog.

Gro

wth

rate

20%

15%

10%

5%

2%

1%

Star

Wildcat

Cash Cows

Dogs

10x 5.0x 3.0x 2.0x 1.0x 0.5x 0.3x 0.2x 0.1x

Relative Market Share

Figure 1: BCG market share – growth rate matrix

The wildcat is a market segment that is at the growth stage of development

whose ultimate business potential is unknown but expected to be good. This segment

demands heavy investments and cash inflows without any immediate generation of

profits or earnings. Investment in this segment is, therefore, to lay the foundation for

future business earnings and cash inflows. The growth decision strategy for such


market is to continue investments towards obtaining a dominant market share. In the

absence of significant market share gains, this segment may require to be redefined or

withdrawn from. The star is the developed product market generating positive

earnings but still requiring continuous investment for increasing their market share.

The near term earning potential for such markets is proven and, therefore, is a strong

contender for growth and management attention. The key strategy for star markets is

to maintain the growth spree despite of low (or even negative) cash inflows. Cows are

the most mature product market segment where growth in market share is low but still

high yielding. This market does not call for growth strategies but need investments to

maintain the market share and positive cash inflows. Such segments are usually

milked for their cash inflows which are diverted to other growth markets demanding

cash outflows. Dogs represent the declining market segments whose attractiveness to

the firm has become diminished owing to their low earning potential. They tend to

have negative cash flows when turned on growth but are positive cash flow

generators when the capacities are deliberately shrunk. The key strategy

recommended for such a market is withdrawal and unlocking of resources or

repositioning of the offerings appropriately to counter competition. In this way, the

BCG matrix provides a system of priorities for investment and allocation of funds

between businesses with the highest priority accorded to the star followed by wild cat

and cash cow.

The BCG matrix has been a widely used technique for planning of business

portfolio (Morrison and Wensley, 1991; Olsen and Ellram, 1997) although its effect

on corporate performance is yet to be conclusively agreed upon. Earlier studies such


as Hambrick, MacMillan and Day (1982) have found that businesses actually differed

in their strategic attributes and performance according to the two dimensions of the

BCG matrix, studies such as by Armstrong and Brodie (1994) have pointed out that

the use of BCG technique could also lead to selection of projects and businesses that

were less profitable. Also, the two dimensions of the matrix are insufficient to define

market exhaustively and many real life observations do not seem to fit well into the

matrix‟s descriptions (Thompson and Strickland, 1983). Subsequently, therefore,

frameworks such as General Electric‟s Attractiveness-Strength Matrix and Shell‟s

Directional Policy matrix (Robinson et al., 1978) have used several factors to

characterize industry attractiveness and business strength thereby enabling

identification of different market positions. These frameworks, however, have been

found to be more complex (Hax and Majluf, 1983) and have found much lesser

application in practice (McDonald, 1990). The strength of the BCG matrix has been

its simplicity, though its application has been prone to oversimplification (Seegar,

1984).

The BCG matrix may not be readily applied to planning process in the

service industry (Carman and Langeard, 1980) and particularly in banking owing to

the intangible nature of input and output markets, which are also often overlapping.

Banks primarily operate in deposit and advance markets that may be related and

accordingly the service output in banking can be viewed differently as intermediation

(where deposit forms the input resource along with establishment and human resource

costs and advances the output) or as financial service (where both deposit and

advance are the service outputs with establishment and human resources as input).


Banks have often contradicted the positive relationship between market share and

profitability (Wind and Mahajan, 1981) and are unlike other businesses where dogs

can be divested easily. Analogous to the BCG matrix, Boussofiane et al. (1991) and

later Camanho and Dyson (1999) have suggested the efficiency-profitability matrix,

for the banking industry. In this matrix, bank branches have been classified into four

quadrants as stars, sleepers, question marks and dogs. Units that are in the star

quadrant are benchmarks for good operating practice. The sleepers are profitable but

inefficient. These units face a favorable business environment but are the prime

candidates for efficiency improvement. Question marks have potential for both

greater efficiency and profitability. Branches in the dog quadrant are operating

efficiently but are relatively low on profitability owing to unfavorable business

environment. Calandro and Lane (2007) and Alexakis and Tsolas (2009) have also

applied the matrix to banking, though not as a portfolio organizing tool, but to study

the competitiveness of banks in the Greek banking industry.

The BCG Matrix, however, can be applied in banking for determining the

relative growth and profit potential for the various markets in which a bank operates.

The strength behind this assertion is drawn from the fact that banks operate through

similar business units in various geographical locations, which operate as

independently but together constitute its market portfolio. Financial need of

households and, therefore, markets for deposit and loans demonstrate life cycle

behavior (Roy, 2003) following changes in demography, income and competition

from initial expansion to maturity and saturation. Old branches often face stagnation

while new branches that address new generation customers with technology based


services may grow rapidly. Planning function in banks comprises mainly the

allocation of growth targets and resource budget to the various deposit and advance

markets depending upon the life cycle stage. Matching fund flows between markets is

a key result area of planning function that might be helped from the use of the matrix.

Besides, regulation does provide for rationalizing and repositioning of loss making or

under-performing branches.

While market growth data can be obtained from external market research

sources, market share data may not be readily available to reveal the business unit‟s

relative share in its service area. For this, the cost of deposit or yield on advances can

be used as a proxy of market share in the deposit and advance markets respectively.

This argument flows from the fact that banks having large market shares in local

markets can exercise power over competitors through their pricing of loans and

deposits. Rhoades (1992) and Edelstein and Morgan (2006) have used loan and

deposit rates as indicators of market sizes of banks. Vajanne (2010) has also inferred

market power from retail deposit interest rates in the Euro market. Average growth

rate and cost (yield) of the bank can be used as heuristic criteria to cut off between the

different performance groups. Combining the deposit and advance market categories

into a strategic planning matrix (Table 1) leads to the relative allocation of operating

budgets to different business units. The allocation is based on the simple logic of

providing the highest incremental budget (indexed as 1.0), from the total cost budget

for the bank, the region having the highest growth potential. The star category of

business areas receive the highest incremental budget of 1.0 compared to the wildcat,

cash cow and dog categories who receive 0.8, 0.5 and 0.3 respectively. Businesses

34JOURNAL OF PERFORMANCE MANAGEMENT 12

have budget allocations based upon both deposit and advance potential (each being

given equal weights) and are normatively expected to have allocations between a

maximum and minimum relative to the next higher and lower category respectively.

Table 1: Relative plan allocation of budget for different business markets

Market Advance

Category Star Wildcat Cash Cow Dog

Dep

osit

Star 1.00 0.80 0.50 0.30

Wildcat 0.80 0.60 0.40 0.20

Cash Cow 0.50 0.40 0.30 0.10

Dog 0.30 0.20 0.10 0.05

4.0 Capacity utilization

The business model of a bank or the design of production and delivery operations also

imposes certain constraints to its growth. Banking is a labor-intensive multi-product

service wherein increased specialization can generate more economic methods of

production. Operating above certain measures of size and scale, therefore, leads to

proficiency of function and technical efficiency (Sarkis, 1999) and provide increased

possibilities of risk diversification. Banks also use other resource inputs such as

branch and technology whose establishment is often determined by competitive

considerations. For example, extent of branching and provision for access to the bank

constitutes the major strategy for differentiation and non-price competition. Banks set

up excess capacity for such reasons and usually face rigidities in adjusting the same

partly because these capacities are sunk or other restrictions such as labor market


rigidities prevail. Consequently, a significant portion of bank inputs are quasi-fixed in

the short run. Depending on level of technology there exist certain minimum efficient

scale of operations above which banks enjoy production economies of scale and

scope to achieve cost reduction from growth in output (Gramley, 1962; Tadesse,

2006). However, increasing returns to scale are experienced only until a certain size

of output (Clark, 1988; Wheelock and Wilson, 1997) above which constraints in one

or more resources may lead to diseconomies of scale. Banks, therefore, have a U

shaped cost capacity curve based on their balance sheet assets.

Capacity based metrics have not been widely applied in planning of banks and

financial service firms (Spaller and McDonald, 2003). Studies to determine scale and

capacity utilization have mainly used econometric methods often with restrictive

assumptions regarding input and output, for example assuming all inputs into the

production function are variable (Hunter and Timme, 1995) or neglecting the

financial service, or off balance sheet output, of the bank (Clark, 1996). It has also

been found that scale economies exist at business unit level, but the same may be

limited at the bank level (Durkin and Elliehausen, 1998). Such dynamics have not

been explained as much in the literature on scale and capacity in banking, whereas it

is important for bank managers to recognize when such limits are reached to prevent

the ensuing diseconomies and increase in cost.

In this regard, the model for estimation of capacity in manufacturing

industries has been found to be useful. The industrial model assumes firms to be

operating with certain fixed inputs in the short run that constrain the growth of output

beyond a certain best operating level. Firms‟ first attempt to increase production


beyond a breakeven point, defined by the level of fixed cost and contribution from

sale, and then attempt to manage their operations within the range of scale economies.

Accordingly, a cost capacity curve can be drawn for the firm‟s operations to indicate

the capacity position at which it is operating. The formulation in Table 2 is derived

from the industrial model to construct the cost-capacity curve for banks using loss in

profits (or increase in costs) along with capacity utilization with reference to a

calculated breakeven level of intermediation volume. Inputs to the model (II, IE, FC,

AV, NII, AOP) can be obtained from annual report of performance of a bank or its

business unit.

Table 2: Formulation for constructing the cost-capacity curve

Interest earning rate (interest earned / total earning assets) ii

Interest expense rate (interest paid / total earning assets) ic

Interest spread ii-ic

Breakeven asset size bevsz cstfx / (ii – ic)

Where Fixed cost cstfx

Current capacity level (as percent of breakeven asset size) (actsz – bevsz) / bevbv

Where Actual asset size actsz

Expected operating profit expop actsz * (ii – ic) + incoth

Where Other income incoth

Profit efficiency (as percent of expected operating profit) (expop – actop) / expop

Where Actual operating profit actop


In the model, bank capacity has been defined in terms of its earning assets, or

the sum of advances and investments, reflecting its key function as financial

intermediary. However, the capacity of a bank in a given economic condition depends

upon its ability to provide a required threshold of access to their services, which is

determined by its fixed operating cost on branch, human resource and other channel

infrastructure. Accordingly, the level of fixed cost can be linked to the intermediation

capacity of the bank. Table 2 presents a model for the breakeven asset size a bank

needs as a financial intermediary, given its in vestment in service facilities. Interest

spread is the contribution from the intermediation function, which is largely

dependent upon external factors. Banks often find it difficult to adjust service

capacity (because of their quasi-fixed nature) with change in demand and hence

attempt to de-link the spread by engaging in off balance sheet activities beyond

financial intermediation. Banks, therefore, have income other fee based and non

interest income that reduces their fixed cost “burden” and enables them to improve

their cost efficiency. This stretch in use of capacity might lead to increase or decrease

of total costs for the bank depending upon the current utilization of capacity. The

cost-capacity curve can be determined by plotting the trend of loss in profit against its

level of capacity use (as percent additional business over breakeven volume).

The above formulation can also be applied to determine the relative cost

capacity plotting of business regions of a bank for any given period (Table 3). On the

Y axis the anticipated loss in profit (or higher incidence of cost) is plotted while the

level of capacity use (over the theoretical breakeven point) is shown in the X axis.

Using the bank average of capacity use level and loss in profit to segregate between


the low and high performance levels, four quadrants can be carved out. The lower left

quadrant indicates to the regions facing stagnation. In the upper left quadrant are the

regions that are resource wasting. These regions are grossly underutilizing their

capacity leading to higher cost burden. The lower right quadrant has regions having

well utilized capacities that are also able to manage with lower cost. The upper right

quadrant has the regions that are clearly stretched at their current use of capacity

leading to high operating costs.

Table 3: Classification of regions based on cost-capacity position

Capacity level

Low High

Loss

in

prof

it

High Under Utilized Stretched

Low

Stagnant Well Utilized

5.0 Industry and organizational context of the study

The study has been situated in the context of banking industry in India where, since

liberalization, although banks have improved their cost efficiency, their profit

efficiency has declined (Sensharma, 2005; Das and Ghosh, 2009). Cost efficiency has

improved as banks expanded their scale of operations (Rezvanian et al., 2008),), but

inefficiencies have been discerned on the revenue side of the banking activity in

particular from losses due to allocative inefficiency (Das and Ghosh, 2009). Kumar

and Gulati (2010) have found out that improvement in cost efficiency has been driven

mostly by technical efficiency rather than allocative efficiency. Das and Kumbhakar


(2010) have even pointed out that many state owned banks are operating far above

their efficient scale and cost savings can be obtained by reducing their size of

operations.

Bank organizations in India are structured into geographically based regions

and branches. Historically, planning in banking has been equated with target setting

and budgeting for the various regions and branches (Mote and Shah, 1972). Mote and

Shah (1972) have criticized planning processes to be mechanistic and lacking formal

use of business environment data. Kaura (1983) found that branch performance

budgets are influenced mainly by (a) past performance of the branch, (b) policy

guidelines of the head office and (c) policy guidelines of regional office.

Environmental data of the branch usually came last in determining the performance

budget. Relying on historical data for past performance, banks have pursued growth

through their already large business units. However, correlation between data of

regional business sizes and net interest spreads (the difference between yield on

advances and cost of deposits) of banks (in 2005-06) indicate to an inverse

relationship implying that the larger regions may have lower spreads (Table 4).

Table 4: Correlation between business size with spread of regions

No

Bank Type

No of

Branches

No of

Regions

Correlation of Size and Spread

Correlation Significance

1 Public 1130 18 -0.644 0.005

2 Public 950 17 -0.475 0.041

3 Public 800 19 -0.860 0.000

3 Cooperative 105 6 -0.767 0.044

4 Private 395 8 -0.214 0.610


The dependence on larger units to spearhead growth and consequent higher

allocation of resources to these units may well be the reason behind the decline of

efficiencies in revenue side. Even as banks adopted long range planning

(Bandopadhyay, 1982; Goyal, 1982), the planning process became even top driven

and removed from the business environment realities of the branch market (Seshadri,

1982; Kamalnayan, 1983). Banks have, therefore, always struggled to ensure that the

budgeting goals of branch offices are compatible with the strategic goals of the bank

as a whole.

ABC Bank, a public sector bank had, in 2005-06, a total business of Rs

350000 million generated from around 1100 branches distributed across 18

geographical regions (denoted later by alphabets from A to R). Geographic regions

being more homogenous aggregations of bank business, has been taken as unit of

analysis. Also, local business planning and resource allocation functions being

located at the regional offices of banks, this level of analysis is meaningful for

practical purpose. ABC Bank has been plagued with problems of stagnating income

growth and declining profitability since the year 2000 and the management has been

seriously considering ways of repositioning the Bank to affect performance

turnaround. Annual planning and performance data for the regions including growth

(planned and actual), cost of deposit, yield on advances and operating costs were

provided by the bank for the years 2005-06. Regional deposit and advance market

growth data have been obtained from publicly available sources (Reserve Bank of

India, 2006).


In Figure 2, the relative performance in the deposit market in respect of

growth (depgrth) and cost (depcost) of deposits for the business regions of ABC Bank

are shown. The upper right box has the deposit markets that are growing at a rate

above the bank average but at higher cost of deposit. These belong to the wildcat

category that needs special attention for development of relationship with customers

and growth of business. In the upper left box are the stars that are the deposit markets

where the bank has high growth as well as the strength of customer relationship and

market power leading to low cost of deposit. They are expected to be the profit

making operations that will lead the growth phase of the bank in the current and near-

term period. The lower left branches are the cash cows that have already established

customer relationships but are unable to grow rapidly owing to market stagnation.

The lower right box contains the dogs whose deposit growth in the current period can

only be at higher cost to the bank. The bank needs to examine whether growth

potential in such markets have totally stagnated or there is a need for repositioning the

branch or bank‟s offerings to regain strength.


4.40 4.60 4.80 5.00

depcost

20.00

30.00

40.00

50.00

60.00

depgrth

A

A

A

A

AA

A

A

A

A

A

A

AAA

A

A

A

A

B

C

DEF

G

H

I

J

K

L

M NO P

Q

R

Figure 2: Deposit Market Growth-Share Matrix for ABC Bank

Similarly, in Figure 3, the relative business unit performances for growth (advgrth)

and yield (advyield) of advances are shown. Interpretation of the matrix can also be

made similarly with the exception that the upper left box indicates to regions having

growth potential but low yield and are, therefore, the wildcat. In the upper right box

are the Stars while the lower right markets are the Cash cows that are earning good

yield but are unable to grow rapidly. The lower left box contains the dogs.


7.00 8.00 9.00 10.00

advyield

20.00

30.00

40.00

50.00

60.00

advgrth

A

A

A

A

A

A

A

A

A

A

A

A

A

A

A

A

AA

A

B

C

D

E

F

G

H

I

J

K

LM

N

O

PQ R

Figure 3: Advance Market Growth-Share Matrix for ABC Bank

The relative positioning of the various regions of the ABC Bank can be drawn

now in respect of their growth and profit potential as in Table 5. The table also

compares the actual plan budgetary allocations to the regions with the normative plan

proposed earlier. As seen from the table, the budgetary allocation does not seem to be

well balanced between the various business regions. In about 72% of the regions,

budgetary provisions seem to be unmatched (either low or high) with their market

potential.


Table 5: Relative budget allocations to the regions of ABC Bank

Market Advance

Category Star Wildcat Cash Cow Dog

Dep

osit

Star

K – 0.70 L – 0.85

Wildcat

B – 0 H – 0.80

G -0.62 C - 0 J – 0 Q – 0.27

Cash Cow

F – 0.35 M – 0 I – 0.37 R - 0.07

O – 0.45

Dog

P – 0.19 D – 0.18 E – 0.25 N – 0.41

A – 1.00

Applying the cost-capacity formulation to annual performance data of various

regions of ABC Bank, the relative cost-capacity positions (loss in profit reported as

“prftloss” and capacity level as “addlcpty”), are obtained as in Figure 4. The Bank has

a large number of regions falling in the stagnant, under utilized or stretched category

while just a few are well utilized.


100.00 200.00 300.00 400.00

addlcpty

100.00

200.00

300.00

400.00

500.00

prftloss

A

A

A

A

A

A

AA

A

A

A

A

A

A

A

A

A

A

A

B

C

D

E

F

GH

I

J

K

L

M

N

O

P

Q

R

Figure 4: Relative cost-capacity positions of the regions of ABC Bank

6.0 Gaps in strategic planning

Taking cue from Boussofiance et al. (1991), the growth-share and cost-

capacity matrices can be combined as in Table 6. In Table 6, the regions of the Bank

are arranged in order of their decreasing size along with the findings from the

application of the BCG matrix informing the mismatches between the normative and

plan budgeting of resources as well as the findings from the application of the cost

capacity matrix informing the efficiency positioning of the regions in respect of their

distance from the economic scale frontier. These findings together allow us to assess

and suggest possible modifications to the target and resource allocation strategy for

the Bank.


Firstly, a large number of regions are in dog categories either in deposit or

advance markets or both. Most of such regions are operating at low level of capacity

underutilizing their resources towards business growth. At least one region, which is

also the largest, is clearly wasting and merits rationalization of branch operations.

Many the dog regions also enjoy a high level of budgetary allocation at the expense

of other business areas having higher growth potential. A very hard view of the

management of such regions is needed to improve the operating performance and

resource utilization or else these will continue to drag down the performance of the

bank. Secondly, cash cow businesses operate at higher level of capacity utilization

without proportionate increase in their costs. Most of such regions have lower

budgetary allocations than required to maintain their position. The budgets for these

regions need to be reviewed and increased to ensure that business and market share is

sustained. Thirdly, stars regions are being overstretched at their current capacity. A

few of them are clearly starved of resources and require higher allocation to sustain

growth. Finally, there are only two wildcats in advances market although there are

few in deposit market. But most of the latter are either poorly performing or lacking

adequate supply of budget resources.

From the above observations it seems that the bank is not well positioned in

new growth markets nor does it seem to be devoting appropriate plan attention and

budgetary support to the available growth areas. Many of its business regions are

already peaked or declining in their potential to provide growth and earnings and the

bank is unable to create or find growth sectors within its definition of business

markets. There also exist significant differences in the business profiles of regions in


their unbalanced exposure to deposit and advance market. Clearly, the business

planning function at the regional levels are performing at less that the required vigor.

Planning in the bank exists as a top down performance budgeting exercise with little

business-led innovation for growth and profit. Consequently, local contingencies of

demand and capacity have been overlooked during planning leading to poor

performance of the bank.

Table 6: Revised budget allocation for business regions

Region

Incremental Budget

Allocation

Cost

Capacity

Position

Revised

Budget

Allocation Normative Plan Actual

A 0.00-0.05 1.00 Stagnating Lower

B 0.50-0.80 0 Stretched Increase

C 0.20-0.40 0 Well utilized Increase

D 0.10-0.20 0.18 Underutilized Retain

E 0.05-0.10 0.25 Stretched Retain

F 0.30-0.50 0.35 Stretched Increase

G 0.40-0.60 0.62 Well utilized Retain

H 0.50-0.80 0.80 Well utilized Retain

I 0.10-0.30 0.37 Underutilized Lower

J 0.10-0.20 0 Stagnating Increase

K 0.80-1.00 0.70 Stretched Increase

L 0.20-0.30 0.85 Underutilized Lower

M 0.10-0.30 0 Well utilized Increase

N 0.05-0.10 0.41 Underutilized Lower

O 0.05-0.10 0.45 Stagnating Lower

P 0.20-0.30 0.19 Underutilized Retain

Q 0.10-0.20 0.27 Stagnating Lower


R 0.10-0.30 0.07 Well utilized Increase

7.0 Conclusion

The study diagnoses certain gaps in the planning process of a bank that

neglects the critical factors of market positioning and capacity utilization, which can

pose as constraints to its profitable growth. It illustrates the difficulties in strategic

planning of a multi-unit business organization, where as the number of units increase,

the planning process might lose the portfolio view. This impedes the synergy gains

causing loss of allocative efficiency. The BCG market share – growth rate matrix has

been applied to assess the growth and profit potential of business regions in regards of

their deposit and advance markets and the superimposition of the industrial model of

cost-capacity position to assess the existence of production economies and resource

requirements. The application of frameworks together indicates to the needed revision

in the budget allocations for the business regions. Although based on a single bank,

the case study does lend certain credibility to the call for revisiting the planning

processes and practices in Indian banks.

The study, however, requires to be taken further using data of a larger sample

of banks and business regions as well as more number of years to confirm the

findings. While a symmetric treatment has been meted out for budgetary allocation to

both deposit and advance markets, the heuristic needs to be improved by fine tuning

the weights for budget allocation. Also, the cut off points for the performance

categories need to be determined. Suggestions for revised allocation need to be finally


tested for their impact on performance. In concluding, the study leads us to

hypothesize that business planning in banks must address both market and capacity

factors together in order to ensure meeting of growth and profit objectives together.

Otherwise, as size becomes the preponderant corporate objective of banks, growth

will become without profit and unsustainable.

References

Acharya, V. V. and Yorulmazer, T. (2008), “Information contagion and bank

herding”, Journal of Money, Credit and Banking, 40 (1): 215-231.

Alexakis, P. and Tsolas, I. (2009), “Competitive analysis of Greek commercial banks

using the relative profitability and growth matrix”, Paper presented at the Sixteenth

Annual Conference of the Multinational Finance Society, June 28- July 1, 2009,

Rethymno, Crete, Greece.

Al Shamsi, F. S., Aly, H. Y. and El-Bassiouni, M. Y. (2009), “Measuring and

explaining the efficiencies of the United Arab Emirates banking system”, Applied

Economics, 1-15.

Ariss, R. T. (2010), “On the implication of market power in banking: Evidence from

developing countries”, Journal of Banking and Finance, 34 (4): 765-775.

Armstrong, J. S. and Brodie, R. J. (1994), “Effects of portfolio planning methods on

decision making: Experimental results”, International Journal of Research in

Marketing, 11: 73-84.

Austin, D. V. (1990), “Practical planning suggestions for community banks”,

Banker’s Magazine, July-August: 78-82.


Bandyopadhyay, R. (1982), “Long-range planning in Indian commercial banks: a

conceptual approach”, Prajnan, 11 (2): 81-100.

Barney, J. B. (1991), “Firm resources and sustained competitive advantage”, Journal

of Management, 17 (1): 99-120.

Berger, A. N. and Hannan, T. H. (1989), “The price concentration relationship in

banking”, Review of Economics and Statistics, 71 (2): 291-299.

Berger, A. N., Hanweck, D. and Humphrey, D. (1987), “Competitive viability in

banking: scale, scope and product mix economies”, Journal of Monetary Economics,

20 (3): 501-520.

Bird, A. (1991), “A 1990s twist on strategic planning”, Bankers Magazine, March –

April, 66-69.

Brissimis, S. N., Delis, M. D. and Tsionas, E. G. (2010), “Technical and allocative

efficiency in European banking”, European Journal of Operational Research, 204

(1): 153-163.

Boussofiance, A., Dyson, R. G. and Thanassoulis, E. (1991), “Applied data

envelopment analysis”, European Journal of Operational Research, 52: 1-15.

Buzzell, R. D., Gale, B. T. and Sultan, R. G. M. (1975), “Market share – a key to

profitability”, Harvard Business Review, January – February: 97-107.

Calandro, J. and Lane, S. (2007), “A new competitive analysis tool: the relative

profitability and growth matrix”, Strategy and Leadership, 35 (2): 30-38.

Camanho, A. S. and Dyson, R. G. (1999), “Efficiency, size, benchmarks, and targets

for bank branches: an application of data envelopment analysis”, Journal of the

Operational Research Society, 50: 903-915.


Carman, J. M. and Langeard, E. (1980) “Growth strategies for service firms”,

Strategic Management Journal, 1 (1): 7-22.

Chang, A., Chaudhuri, S., and Jayaratne, J. (1997), “Rational herding and the spatial

clustering of bank branches: an empirical analysis” Federal Reserve Bank of New

York Research Paper # 9724.

Clark, J. A. (1988), “Economies of scale and scope at depository financial

institutions: A review of literature”, Federal Reserve Bank of Kansas City Economic

Review, September – October: 16-33.

Clark, J. A. (1996), “Economic cost, scale efficiency and competitive viability in

banking.” Journal of Money, Credit, and Banking, 28 (3): 342-364.

Das, A. and Ghosh, S. (2009), “Financial deregulation and profit efficiency: a non

parametric analysis of Indian banks”, Journal of Economics and Business, 61 (6):

509-528.

Das, A. and Kumbhkar, S. C. (2010), “Productivity and efficiency dynamics in Indian

banking: an input distance function approach incorporating quality of inputs and

outputs”, Journal of Applied Econometrics (article online in advance of print)

Demstez, H. (1973), “Industry structure, market rivalry and public policy”, Journal of

Law and Economics, 16 (1): 1-9.

DeYoung, R. (1994), “X-efficiency and management quality in commercial banks”,

Office of the Comptroller of the Currency, E&PA Working paper 94-1.

Durkin, T. A. and Elliehausen, G. E. (1998), “The cost structure of the consumer

finance industry”, Journal of Financial Services Research, 13 (1): 71-86.


Edelstein, P. and Morgan, D. P. (2006), “Local or state: Evidence on bank market size

using branch prices”, Economic Policy Review, 12 (1): 15-25.

Gallick, E. C. (1976), “Bank profitability and bank size”, Monthly Review, Federal

Reserve Bank of Kansas City, 11-16.

Gilbert, A. (1984), “Studies of bank market structure and competition: A review and

evaluation”, Journal of Money Credit and Banking, 16: 617-644.

Goddard, J., Molyneux, P. and Wilson, J. O. S. (2004), “The profitability of European

banks: A cross sectional and dynamic panel analysis”, The Manchester School, 72

(3): 363-381.

Goyal, R. P. (1982), “ Long-range planning: The State Bank experience”, Prajnan, 11

(2): 101-116.

Gramley, L. E. (1962). Scale Economies in Banking. Federal Reserve Bank of Kansas

City.

Gup, B. E. and Whitehead, D. D. (1989), “Strategic planning in banks: Does it pay?”,

Long Range Planning, 22: 124-130.

Hambrick, D. C., Macmillan, I. C. and Day, D. L. (1982), “Strategic attributes and

performance in the BCG matrix – a PIMS based analysis of industrial products

businesses”, Academy of Management Journal, 25 (3): 510-531.

Haslem, J. A. (1968), “A statistical analysis of the relative profitability of commercial

banks”, Journal of Finance, 23 (1): 167-176.

Hax, A. C. and Majluf, N. S. (1983), “The use of the industry attractiveness-business

strength matrix in strategic planning”, Interfaces, 13 (2): 54-71.

Henderson, B. (1968), “The product portfolio”, BCG Perspectives,66.


Hopkins, W. E. and Hopkins, S. A. (1997), “Strategic Planning – financial

performance relationships in banks: A causal examination”, Strategic Management

Journal, 18 (8): 635-651.

Hughes, J. P. and Mester, L. J. (1993), “A quality- and risk-adjusted cost function for

banks: evidence on the too big to fail doctrine”, Journal of Productivity Analysis, 4:

293-315.

Hunter, W. C. and Timme, S. G. (1995), “Core deposits and physical capital: a

reexamination of bank scale economies and efficiency with quasi fixed inputs”,

Journal of Money, Credit and Banking, 27 (1): 165-185.

Jeon, Y. and Miller, S. M. (2005), “Bank performance: Market power or efficient

structure”, Working Paper 2005-23, University of Connecticut.

Kamal Nayan (1983) “Performance budgeting and performance appraisal: a case

study”, Prajnan, 12 (4): 327-336.

Kaura, M. N. (1983), “Performance budget settlement process in public sector

banks”, Vikalpa, 8 (1): 35-43.

Kosmidou, K., Tanna, S., and Pasiouras, F. (2005), “Determinants of Profitability of

domestic UK Commercial Banks: Panel Evidence from the Period 1995-2002,”

Money Macro and Finance (MMF) Research Group Conference.

Kumar, S. and Gulati, R. (2010), “Dynamics of cost efficiency in Indian public sector

banks: a post-deregulation experience”, Paper presented in the Twelfth Annual

Conference on Money and Finance in the Indian Economy, 11-12 March 2010, Indira

Gabdhi Institute of Development Research, Mumbai


Larreche, J-C. (1980), “On limitations of positive market share-profitability

relationships: The case of the French banking industry”. Educators’ Conference

Proceedings. Chicago: American Marketing Association.

Lovell, C. A. K. and Pastor, J. T. (1997), “Target setting: An application to a bank

branch network”, European Journal of Operational Research, 98 (2): 290-299.

Maudos, J. (1998) “Market structure and performance in Spanish banking using a

direct measure of efficiency”, Applied Financial Economics, 8 (2): 191-200.

McDonald, M. (1990), “Some methodological comments on the Directional Policy

Matrix”, Journal of Marketing Management, 6: 62-65.

Mendes, V. and Rebelo, J. (2003), “Structure and performance in the Portuguese

banking industry in the nineties”, Portuguese Economic Journal, 2 (1): 53-68.

Micco, A., Panizza, U. and Yanez, M. (2007), “Bank ownership and performance:

does politics matter?”, Journal of Bank Finance, 31 (1): 219-241.

Mintzberg, H. (1987), “The strategy concept I: Five Ps for strategy”, California

Management Review, Fall, 11-24.

Molyneux, P. and Thornton, J. (1992), “Determinants of European bank profitability:

A note”, Journal of Banking and Finance, 16: 1173-1178.

Morrison, A. and Wensley, R. (1991), “Boxed up or boxed in ?: A short history of the

Boston Consulting Group market share/ growth matrix”, Journal of Marketing

Management, 7(2): 105-209.

Mote, V. L. and Shah, B. G. (1972), “Concept of planning: a case study”, Economic

and Political Weekly, 7 (9): M7 - M14.


Newkirk-Moore, S. (1995), “Strategic planning: Steering the small community bank

to profitability”, Strategic Change, 4 (6): 333-342.

Olsen, R. F. and Ellram, L. M. (1997), “A portfolio approach to supplier

relationships”, Industrial Marketing Management, 26: 101-113.

Peltzman, S. (1977), “The gains and losses from industrial concentration”, Journal of

Law and Economics, 20: 229-263.

Penrose, E. T. (1959), The Theory of the Growth of the Firm, New York: John Wiley.

Pilloff, S. J. and Rhoades, S. A. (2002), “Structure and profitability in banking

markets, Review of Industrial Organization, 20 (1): 81-98.

Porter, M. E. (1980), Competitive Strategy, New York: Free Press.

Prasad, B. S. (1984), “The paradox of planning in banks”, Bankers Magazine, pp. 77-

81.

Punt, L. and Rooij, M. (2001), “The profit-structure relationship and mergers in the

European banking industry”. Staff Report 58, De Nederlandsche Bank.

Rapoport, M. (2007), „Net interest margin: the role of bank size and business focus”,

North American Financial Institutions Weekly, 2 (11).

Rezvanian, R., Rao, N. and Mehdian, S. M. (2008) “Efficiency change, technological

progress and productivity growth of private, public and foreign banks in India:

evidence from the post-liberalization era”, Applied Financial Economics, 18 (9): 701-

713.

Reserve Bank of India (2006), Basic Statistical Returns of Scheduled Commercial

Banks in India. Vol 35.


Rhoades, S. A. (1982), “Structure performance studies in banking: an updated

summary and evaluation”, Staff Study No 119, Board of Governors of the Federal

Reserve System.

Rhoades, S. A. (1992), “Evidence on the size of banking markets from mortgage loan

rates in twenty cities”, Federal Reserve Bulletin, 78: 117-118.

Robinson, S. J. Q., Hichens, R. E. and Wade, D. P. (1978), “The Directional Policy

Matrix – tool for strategic planning”, Long Range Planning, 2 (3): 8-15.

Rogers, P. R., Miller, A. and Judge, W. Q. (1999), “Using information-processing

theory to understand planning/ performance relationships in the context of strategy”,

Strategic management Journal, 20: 567-577.

Rotheli, T. F. (2001), “Competition, herd behavior, and credit cycles: evidence from

Swiss Banks”, Journal of Economics and Business, 53 (6): 585-592.

Roy, D. (2003). Dynamics of bank deposits: Developing states in India. New Delhi:

Social Science Press.

Sarkis, Z. (1992), General Cost Structure Analysis: Theory and application to the

banking industry. Boston: Kluwer Academic Publishers.

Schwartz, W. (2007), “Is size an advantage for bank funding?: only some of the

time”, North American Financial Institutions Weekly, 2 (9).

Seegar, J. (1984), “Reversing the images of BCG‟s growth/share matrix”, Strategic

Managing Journal, 5(1): 93-97.

Sensharma, R. (2005), “Cost and profit efficiency of Indian banks during 1986-

2003”, Economic and Political Weekly, 1198-1208.


Seshardi, I. J. H. (1982), “Role of environmental analysis in bank management”, 11

(3): 195-208.

Smirlock, M. (1985), “Evidence of the (non-) relationship between concentration and

profitability in banking”, Journal of Money, Credit and Banking, 17: 69-83.

Spaller, R. and McDonald, R. (2003), “Capacity based costing in banking”, Journal

of Bank Cost and Management Accounting, 16 (1): 3-15.

Tadesse, S. (2006), “Consolidation, scale economies and technological change in

Japanese banking”, Journal of International Financial Markets, Institutions and

Money, 16 (5): 425-445.

Thompson, A. A. and Strickland, A. J. (1983), Strategy formulation and

implementation: Tasks of the General Manager. Texas: Business Publications.

Tregenna, F. (2009), “The fat years: the structure and profitability of the US banking

sector in the pre-crisis period”, Cambridge Journal of Economics, 33 (4): 609-632.

Uchida, H. and Nakagawa, R. (2007), “Herd behavior in the Japanese loan market:

evidence from bank panel data”, Journal of Financial Intermediation, 16 (4).

Vajanne, L. (2009), “Inferring market power from retail deposit interest rates in Euro

area”, Bank of Finland Research Discussion Paper Number 27.

Wheelock, D. C. and Wilson, P. W. (1997), “New evidence on returns to scale and

product mix among U.S. commercial banks”, Journal of Monetary Economics, 47 (3):

653-674.

Whitehead, D. D. and Gup B. E. (1985), “Bank and thrift profitability: Does strategic

planning really pay?” Economic Review, pp. 15-25.

36

Wind, Y. and Mahajan, V. (1981), “Designing product and business portfolios”,

Harvard Business Review, 59 (1): 155-165.

Wood, D. R. (1980), “Long range planning in large United States banks”, Long

Range Planning, 13 (3): 91-98.

Wood, D. R. and Laforge, R. L. (1979), „The impact of comprehensive planning on

financial performance‟, The Academy of Management Journal, Vol. 22, No. (3): 516-

526.


1. Journal

Documents

Transcript of 1. Journal